Overview

Dataset statistics

Number of variables10
Number of observations2200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory172.0 KiB
Average record size in memory80.1 B

Variable types

Numeric8
Categorical2

Alerts

CROP is uniformly distributedUniform
TEMPERATURE has unique valuesUnique
HUMIDITY has unique valuesUnique
ph has unique valuesUnique
RAINFALL has unique valuesUnique
N_SOIL has 27 (1.2%) zerosZeros

Reproduction

Analysis started2024-05-10 19:04:03.846173
Analysis finished2024-05-10 19:04:09.774857
Duration5.93 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

N_SOIL
Real number (ℝ)

ZEROS 

Distinct137
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.551818
Minimum0
Maximum140
Zeros27
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T00:34:09.877930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q121
median37
Q384.25
95-th percentile116
Maximum140
Range140
Interquartile range (IQR)63.25

Descriptive statistics

Standard deviation36.917334
Coefficient of variation (CV)0.73028696
Kurtosis-1.0582399
Mean50.551818
Median Absolute Deviation (MAD)26
Skewness0.50972137
Sum111214
Variance1362.8895
MonotonicityNot monotonic
2024-05-11T00:34:09.993967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 44
 
2.0%
40 44
 
2.0%
27 42
 
1.9%
39 41
 
1.9%
31 41
 
1.9%
32 39
 
1.8%
37 39
 
1.8%
34 38
 
1.7%
29 37
 
1.7%
36 35
 
1.6%
Other values (127) 1800
81.8%
ValueCountFrequency (%)
0 27
1.2%
1 20
0.9%
2 26
1.2%
3 21
1.0%
4 27
1.2%
5 27
1.2%
6 29
1.3%
7 25
1.1%
8 29
1.3%
9 33
1.5%
ValueCountFrequency (%)
140 3
0.1%
139 1
 
< 0.1%
136 2
 
0.1%
135 1
 
< 0.1%
134 2
 
0.1%
133 4
0.2%
132 2
 
0.1%
131 6
0.3%
130 1
 
< 0.1%
129 3
0.1%

P_SOIL
Real number (ℝ)

Distinct117
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.362727
Minimum5
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T00:34:10.110378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q128
median51
Q368
95-th percentile133
Maximum145
Range140
Interquartile range (IQR)40

Descriptive statistics

Standard deviation32.985883
Coefficient of variation (CV)0.61814462
Kurtosis0.86027876
Mean53.362727
Median Absolute Deviation (MAD)20
Skewness1.0107725
Sum117398
Variance1088.0685
MonotonicityNot monotonic
2024-05-11T00:34:10.223734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 56
 
2.5%
58 48
 
2.2%
56 46
 
2.1%
55 44
 
2.0%
57 42
 
1.9%
59 41
 
1.9%
18 39
 
1.8%
21 39
 
1.8%
25 37
 
1.7%
40 37
 
1.7%
Other values (107) 1771
80.5%
ValueCountFrequency (%)
5 22
1.0%
6 24
1.1%
7 25
1.1%
8 20
0.9%
9 17
0.8%
10 14
0.6%
11 21
1.0%
12 17
0.8%
13 15
0.7%
14 19
0.9%
ValueCountFrequency (%)
145 8
0.4%
144 12
0.5%
143 10
0.5%
142 7
0.3%
141 7
0.3%
140 12
0.5%
139 12
0.5%
138 10
0.5%
137 7
0.3%
136 10
0.5%

K_SOIL
Real number (ℝ)

Distinct73
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.149091
Minimum5
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T00:34:10.344417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile15
Q120
median32
Q349
95-th percentile199
Maximum205
Range200
Interquartile range (IQR)29

Descriptive statistics

Standard deviation50.647931
Coefficient of variation (CV)1.051898
Kurtosis4.4493544
Mean48.149091
Median Absolute Deviation (MAD)13
Skewness2.3751672
Sum105928
Variance2565.2129
MonotonicityNot monotonic
2024-05-11T00:34:10.469024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 90
 
4.1%
22 87
 
4.0%
15 86
 
3.9%
20 80
 
3.6%
25 78
 
3.5%
19 77
 
3.5%
21 74
 
3.4%
18 72
 
3.3%
45 65
 
3.0%
23 63
 
2.9%
Other values (63) 1428
64.9%
ValueCountFrequency (%)
5 8
0.4%
6 9
0.4%
7 5
0.2%
8 12
0.5%
9 12
0.5%
10 12
0.5%
11 8
0.4%
12 9
0.4%
13 7
0.3%
14 9
0.4%
ValueCountFrequency (%)
205 18
0.8%
204 22
1.0%
203 22
1.0%
202 14
0.6%
201 18
0.8%
200 14
0.6%
199 14
0.6%
198 15
0.7%
197 24
1.1%
196 21
1.0%

TEMPERATURE
Real number (ℝ)

UNIQUE 

Distinct2200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.616244
Minimum8.8256747
Maximum43.675493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T00:34:10.585051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8.8256747
5-th percentile17.915085
Q122.769375
median25.598693
Q328.561654
95-th percentile34.056636
Maximum43.675493
Range34.849818
Interquartile range (IQR)5.7922793

Descriptive statistics

Standard deviation5.0637486
Coefficient of variation (CV)0.19767725
Kurtosis1.2325549
Mean25.616244
Median Absolute Deviation (MAD)2.9015036
Skewness0.18493273
Sum56355.736
Variance25.64155
MonotonicityNot monotonic
2024-05-11T00:34:10.707000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.87974371 1
 
< 0.1%
29.48069921 1
 
< 0.1%
29.94349168 1
 
< 0.1%
28.03306461 1
 
< 0.1%
29.8843055 1
 
< 0.1%
27.7058373 1
 
< 0.1%
29.78714005 1
 
< 0.1%
28.57819995 1
 
< 0.1%
27.51492243 1
 
< 0.1%
27.72653142 1
 
< 0.1%
Other values (2190) 2190
99.5%
ValueCountFrequency (%)
8.825674745 1
< 0.1%
9.467960445 1
< 0.1%
9.535585543 1
< 0.1%
9.724457611 1
< 0.1%
9.851242629 1
< 0.1%
9.949929082 1
< 0.1%
10.01081312 1
< 0.1%
10.16431299 1
< 0.1%
10.2708877 1
< 0.1%
10.35609594 1
< 0.1%
ValueCountFrequency (%)
43.67549305 1
< 0.1%
43.36051537 1
< 0.1%
43.30204933 1
< 0.1%
43.08022702 1
< 0.1%
43.03714283 1
< 0.1%
42.93605359 1
< 0.1%
42.93368602 1
< 0.1%
42.92325255 1
< 0.1%
42.84609252 1
< 0.1%
42.54744013 1
< 0.1%

HUMIDITY
Real number (ℝ)

UNIQUE 

Distinct2200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.481779
Minimum14.25804
Maximum99.981876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T00:34:10.829584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum14.25804
5-th percentile19.374917
Q160.261953
median80.473146
Q389.948771
95-th percentile94.368844
Maximum99.981876
Range85.723836
Interquartile range (IQR)29.686818

Descriptive statistics

Standard deviation22.263812
Coefficient of variation (CV)0.31146135
Kurtosis0.30213407
Mean71.481779
Median Absolute Deviation (MAD)12.103236
Skewness-1.0917079
Sum157259.91
Variance495.67731
MonotonicityNot monotonic
2024-05-11T00:34:10.943172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.00274423 1
 
< 0.1%
90.33698678 1
 
< 0.1%
93.90741192 1
 
< 0.1%
91.47355778 1
 
< 0.1%
94.0371147 1
 
< 0.1%
92.91185695 1
 
< 0.1%
94.65343534 1
 
< 0.1%
92.86597437 1
 
< 0.1%
94.96218673 1
 
< 0.1%
92.00687531 1
 
< 0.1%
Other values (2190) 2190
99.5%
ValueCountFrequency (%)
14.25803981 1
< 0.1%
14.27327988 1
< 0.1%
14.2804191 1
< 0.1%
14.32313811 1
< 0.1%
14.33847406 1
< 0.1%
14.42457525 1
< 0.1%
14.44008871 1
< 0.1%
14.44228303 1
< 0.1%
14.69765308 1
< 0.1%
14.70085967 1
< 0.1%
ValueCountFrequency (%)
99.98187601 1
< 0.1%
99.96906006 1
< 0.1%
99.84671638 1
< 0.1%
99.7240104 1
< 0.1%
99.65809151 1
< 0.1%
99.64573002 1
< 0.1%
99.64328526 1
< 0.1%
99.34854917 1
< 0.1%
99.18843684 1
< 0.1%
98.80313612 1
< 0.1%

ph
Real number (ℝ)

UNIQUE 

Distinct2200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4694801
Minimum3.5047523
Maximum9.9350907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T00:34:11.056396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.5047523
5-th percentile5.4351117
Q15.9716928
median6.4250453
Q36.9236426
95-th percentile7.7484174
Maximum9.9350907
Range6.4303384
Interquartile range (IQR)0.95194982

Descriptive statistics

Standard deviation0.77393769
Coefficient of variation (CV)0.11962904
Kurtosis1.6555815
Mean6.4694801
Median Absolute Deviation (MAD)0.4743912
Skewness0.28392944
Sum14232.856
Variance0.59897954
MonotonicityNot monotonic
2024-05-11T00:34:11.253046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.502985292 1
 
< 0.1%
6.640470863 1
 
< 0.1%
6.251420275 1
 
< 0.1%
6.274452811 1
 
< 0.1%
6.135996372 1
 
< 0.1%
6.194090172 1
 
< 0.1%
6.327822962 1
 
< 0.1%
6.212567211 1
 
< 0.1%
6.685553129 1
 
< 0.1%
6.350623739 1
 
< 0.1%
Other values (2190) 2190
99.5%
ValueCountFrequency (%)
3.504752314 1
< 0.1%
3.510404312 1
< 0.1%
3.5253661 1
< 0.1%
3.532008668 1
< 0.1%
3.558822825 1
< 0.1%
3.692863601 1
< 0.1%
3.71105919 1
< 0.1%
3.793575185 1
< 0.1%
3.808429173 1
< 0.1%
3.828031463 1
< 0.1%
ValueCountFrequency (%)
9.93509073 1
< 0.1%
9.926212291 1
< 0.1%
9.679240873 1
< 0.1%
9.45949344 1
< 0.1%
9.416003106 1
< 0.1%
9.406887533 1
< 0.1%
9.392694614 1
< 0.1%
9.254089438 1
< 0.1%
9.160691747 1
< 0.1%
9.112771682 1
< 0.1%

RAINFALL
Real number (ℝ)

UNIQUE 

Distinct2200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.46366
Minimum20.211267
Maximum298.56012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T00:34:11.361969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20.211267
5-th percentile33.823512
Q164.551686
median94.867624
Q3124.26751
95-th percentile209.54244
Maximum298.56012
Range278.34885
Interquartile range (IQR)59.715822

Descriptive statistics

Standard deviation54.958389
Coefficient of variation (CV)0.53118545
Kurtosis0.60707929
Mean103.46366
Median Absolute Deviation (MAD)30.103324
Skewness0.96575635
Sum227620.04
Variance3020.4245
MonotonicityNot monotonic
2024-05-11T00:34:11.469879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202.9355362 1
 
< 0.1%
26.0365768 1
 
< 0.1%
20.39020503 1
 
< 0.1%
21.17924769 1
 
< 0.1%
21.0000988 1
 
< 0.1%
22.06207161 1
 
< 0.1%
27.8659442 1
 
< 0.1%
27.5987178 1
 
< 0.1%
21.01796432 1
 
< 0.1%
20.21126747 1
 
< 0.1%
Other values (2190) 2190
99.5%
ValueCountFrequency (%)
20.21126747 1
< 0.1%
20.36001144 1
< 0.1%
20.39020503 1
< 0.1%
20.49035619 1
< 0.1%
20.66127836 1
< 0.1%
20.76212031 1
< 0.1%
20.76223014 1
< 0.1%
20.76582087 1
< 0.1%
20.88620369 1
< 0.1%
21.0000988 1
< 0.1%
ValueCountFrequency (%)
298.5601175 1
< 0.1%
298.4018471 1
< 0.1%
295.9248796 1
< 0.1%
295.6094492 1
< 0.1%
291.2986618 1
< 0.1%
290.6793783 1
< 0.1%
287.5766935 1
< 0.1%
286.5083725 1
< 0.1%
285.2493645 1
< 0.1%
284.4364567 1
< 0.1%

STATE
Categorical

Distinct26
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size17.3 KiB
Uttar Pradesh
574 
Kerala
274 
Tamil Nadu
183 
Punjab
180 
Maharashtra
162 
Other values (21)
827 

Length

Max length19
Median length17
Mean length9.8486364
Min length3

Characters and Unicode

Total characters21667
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndaman and Nicobar
2nd rowAndaman and Nicobar
3rd rowAndaman and Nicobar
4th rowAndaman and Nicobar
5th rowAndaman and Nicobar

Common Values

ValueCountFrequency (%)
Uttar Pradesh 574
26.1%
Kerala 274
12.5%
Tamil Nadu 183
 
8.3%
Punjab 180
 
8.2%
Maharashtra 162
 
7.4%
West Bengal 124
 
5.6%
Gujarat 113
 
5.1%
Himachal Pradesh 102
 
4.6%
Odisha 86
 
3.9%
Haryana 65
 
3.0%
Other values (16) 337
15.3%

Length

2024-05-11T00:34:11.588351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 727
22.2%
uttar 574
17.6%
kerala 274
 
8.4%
tamil 183
 
5.6%
nadu 183
 
5.6%
punjab 180
 
5.5%
maharashtra 162
 
5.0%
west 124
 
3.8%
bengal 124
 
3.8%
gujarat 113
 
3.5%
Other values (22) 626
19.1%

Most occurring characters

ValueCountFrequency (%)
a 4573
21.1%
r 2266
10.5%
t 1676
 
7.7%
h 1378
 
6.4%
e 1277
 
5.9%
s 1264
 
5.8%
d 1114
 
5.1%
1070
 
4.9%
P 914
 
4.2%
l 710
 
3.3%
Other values (26) 5425
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4573
21.1%
r 2266
10.5%
t 1676
 
7.7%
h 1378
 
6.4%
e 1277
 
5.9%
s 1264
 
5.8%
d 1114
 
5.1%
1070
 
4.9%
P 914
 
4.2%
l 710
 
3.3%
Other values (26) 5425
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4573
21.1%
r 2266
10.5%
t 1676
 
7.7%
h 1378
 
6.4%
e 1277
 
5.9%
s 1264
 
5.8%
d 1114
 
5.1%
1070
 
4.9%
P 914
 
4.2%
l 710
 
3.3%
Other values (26) 5425
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4573
21.1%
r 2266
10.5%
t 1676
 
7.7%
h 1378
 
6.4%
e 1277
 
5.9%
s 1264
 
5.8%
d 1114
 
5.1%
1070
 
4.9%
P 914
 
4.2%
l 710
 
3.3%
Other values (26) 5425
25.0%

CROP_PRICE
Real number (ℝ)

Distinct502
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2689.2282
Minimum2
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T00:34:11.702100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile429.5
Q1950
median1825
Q33500
95-th percentile7101
Maximum120000
Range119998
Interquartile range (IQR)2550

Descriptive statistics

Standard deviation3710.3613
Coefficient of variation (CV)1.3797123
Kurtosis461.36588
Mean2689.2282
Median Absolute Deviation (MAD)1075
Skewness15.926745
Sum5916302
Variance13766781
MonotonicityNot monotonic
2024-05-11T00:34:11.824947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 57
 
2.6%
1000 56
 
2.5%
500 55
 
2.5%
3000 52
 
2.4%
700 51
 
2.3%
2000 50
 
2.3%
900 48
 
2.2%
800 44
 
2.0%
2500 43
 
2.0%
1400 40
 
1.8%
Other values (492) 1704
77.5%
ValueCountFrequency (%)
2 4
0.2%
3 4
0.2%
4 6
0.3%
5 3
0.1%
6 2
 
0.1%
8 2
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
120 1
 
< 0.1%
ValueCountFrequency (%)
120000 1
< 0.1%
32500 1
< 0.1%
29250 1
< 0.1%
29000 1
< 0.1%
27750 1
< 0.1%
26500 1
< 0.1%
22250 1
< 0.1%
21750 1
< 0.1%
21500 1
< 0.1%
20500 1
< 0.1%

CROP
Categorical

UNIFORM 

Distinct22
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.3 KiB
Rice
 
100
Maize
 
100
ChickPea
 
100
KidneyBeans
 
100
PigeonPeas
 
100
Other values (17)
1700 

Length

Max length11
Median length9
Mean length7.1363636
Min length4

Characters and Unicode

Total characters15700
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRice
2nd rowRice
3rd rowRice
4th rowRice
5th rowRice

Common Values

ValueCountFrequency (%)
Rice 100
 
4.5%
Maize 100
 
4.5%
ChickPea 100
 
4.5%
KidneyBeans 100
 
4.5%
PigeonPeas 100
 
4.5%
MothBeans 100
 
4.5%
MungBean 100
 
4.5%
Blackgram 100
 
4.5%
Lentil 100
 
4.5%
Pomegranate 100
 
4.5%
Other values (12) 1200
54.5%

Length

2024-05-11T00:34:11.929997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rice 100
 
4.5%
maize 100
 
4.5%
jute 100
 
4.5%
cotton 100
 
4.5%
coconut 100
 
4.5%
papaya 100
 
4.5%
orange 100
 
4.5%
apple 100
 
4.5%
muskmelon 100
 
4.5%
watermelon 100
 
4.5%
Other values (12) 1200
54.5%

Most occurring characters

ValueCountFrequency (%)
e 2100
 
13.4%
a 2000
 
12.7%
n 1600
 
10.2%
o 1100
 
7.0%
t 800
 
5.1%
g 600
 
3.8%
i 600
 
3.8%
r 500
 
3.2%
B 500
 
3.2%
l 500
 
3.2%
Other values (22) 5400
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2100
 
13.4%
a 2000
 
12.7%
n 1600
 
10.2%
o 1100
 
7.0%
t 800
 
5.1%
g 600
 
3.8%
i 600
 
3.8%
r 500
 
3.2%
B 500
 
3.2%
l 500
 
3.2%
Other values (22) 5400
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2100
 
13.4%
a 2000
 
12.7%
n 1600
 
10.2%
o 1100
 
7.0%
t 800
 
5.1%
g 600
 
3.8%
i 600
 
3.8%
r 500
 
3.2%
B 500
 
3.2%
l 500
 
3.2%
Other values (22) 5400
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2100
 
13.4%
a 2000
 
12.7%
n 1600
 
10.2%
o 1100
 
7.0%
t 800
 
5.1%
g 600
 
3.8%
i 600
 
3.8%
r 500
 
3.2%
B 500
 
3.2%
l 500
 
3.2%
Other values (22) 5400
34.4%

Interactions

2024-05-11T00:34:08.688010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.222414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.856159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.488843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.200388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.838234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.438981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.062044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.771212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.297194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.928403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.574482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.283915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.920319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.521925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.147546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.854569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.380463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.004187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.648503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.367064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.994396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.596848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.224401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:09.044410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.459605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.095211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.729198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.448122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.077144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.682308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.305105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:09.131544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.542882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.171154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.809705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.529063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.152882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.754597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.377290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:09.209352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.617892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.246955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.882276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.598325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.224653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.822658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.452213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:09.290647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.698611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.327132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.959729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.678526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.290293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.903524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.531319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:09.359208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:04.778643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:05.406992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.117845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:06.756912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.360268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:07.978883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-11T00:34:08.606856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-11T00:34:09.460017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T00:34:09.587164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

N_SOILP_SOILK_SOILTEMPERATUREHUMIDITYphRAINFALLSTATECROP_PRICECROP
090424320.87974482.0027446.502985202.935536Andaman and Nicobar7000Rice
185584121.77046280.3196447.038096226.655537Andaman and Nicobar5000Rice
260554423.00445982.3207637.840207263.964248Andaman and Nicobar7000Rice
374354026.49109680.1583636.980401242.864034Andaman and Nicobar7000Rice
478424220.13017581.6048737.628473262.717340Andaman and Nicobar120000Rice
569374223.05804983.3701187.073454251.055000Andaman and Nicobar3500Rice
669553822.70883882.6394145.700806271.324860Andaman and Nicobar7500Rice
794534020.27774482.8940865.718627241.974195Andaman and Nicobar6500Rice
889543824.51588183.5352166.685346230.446236Andaman and Nicobar10000Rice
968583823.22397483.0332276.336254221.209196Andaman and Nicobar11000Rice
N_SOILP_SOILK_SOILTEMPERATUREHUMIDITYphRAINFALLSTATECROP_PRICECROP
2190103403027.30901855.1962246.348316141.483164West Bengal3800Coffee
2191118313427.54823062.8817926.123796181.417081West Bengal1400Coffee
2192106213525.62735557.0415117.428524188.550654West Bengal6500Coffee
2193116383423.29250350.0455706.020947183.468585West Bengal1600Coffee
219497352624.91461053.7414476.334610166.254931West Bengal5600Coffee
2195107343226.77463766.4132696.780064177.774507West Bengal1000Coffee
219699152727.41711256.6363626.086922127.924610West Bengal800Coffee
2197118333024.13179767.2251236.362608173.322839West Bengal560Coffee
2198117323426.27241852.1273946.758793127.175293West Bengal1500Coffee
2199104183023.60301660.3964756.779833140.937041West Bengal1400Coffee